Malta

The recent breakthroughs in Artificial Intelligence (AI) can be attributed to the remarkable performance of Large Language Models (LLMs) across a spectrum of research areas (e.g., machine translation, question-answering, automatic speech recognition, text-to-speech generation) and application domains (e.g., business, law, healthcare, education, and psychology). The success of these LLMs largely depends on specific training techniques, most notably instruction tuning, RLHF, and subsequent prompting to achieve the desired output. As the development of such LLMs continues to increase in both closed and open settings, evaluation has become crucial for understanding their generalization capabilities across different tasks, modalities, languages, and dialects. This evaluation process is tightly coupled with prompting, which plays a key role in obtaining better outputs. There has been attempts to evaluate such models focusing on diverse tasks, languages, and dialects, which suggests that the capabilities of LLMs are still limited for medium-to-low-resource languages due to the lack of representative datasets. The tutorial offers an overview of this emerging research area. We explore the capabilities of LLMs in terms of their performance, zero- and few-shot settings, fine-tuning, instructions tuning, and close vs. open models with a special emphasis on low-resource settings. In addition to LLMs for standard NLP tasks, we will focus on speech and multimodality.

EACL 2024

LLMs for Low Resource Languages in Multilingual, Multimodal and Dialectal Settings

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

tutorial

Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
#### [Conference Handbook](https://drive.google.com/file/d/13Wn38q0yev6U3RTgnf4Tn5T9GuAuZ-xW/view?usp=sharing) *downloadable*

To access the EACL 2024 event page you need to register [**here**](https://acl.swoogo.com/EACL2024). 

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As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

Human processing of nonlocal syntactic dependencies requires engagement of limited working memory for encoding, maintenance, and retrieval. This process creates an evolutionary pressure for language to be structured in a way that keeps the subparts of a dependency closer to each other, an efficiency principle termed dependency locality. The current study proposes that such a dependency locality pressure can be modulated by the surprisal of the antecedent, defined as the first part of a dependency, due to strategic allocation of working memory. In particular, antecedents with novel and unpredictable information are prioritized for memory encoding, receiving more robust representation against memory interference and decay, and thus are more capable of handling longer dependency length. We examine this claim by examining dependency corpora of six languages (Danish, English, Italian, Mandarin, Russian, and Spanish), with word surprisal generated from GPT-3 language model. In support of our hypothesis, we find evidence for a positive correlation between dependency length and the antecedent surprisal in most of the languages in our analyses.

Syntactic dependency length shaped by strategic memory allocation

Recent years have brought significant advances to Natural Language Processing (NLP), which enabled fast progress in the field of computational job market analysis. Core tasks in this application domain are skill extraction and classification from job postings. Because of its quick growth and its interdisciplinary nature, there is no exhaustive assessment of this field. This survey aims to fill this gap by providing a comprehensive overview of deep learning methodologies, datasets, and terminologies specific to NLP-driven skill extraction. Our comprehensive cataloging of publicly available datasets addresses the lack of consolidated information on dataset creation and characteristics. 
Finally, the focus on terminology addresses the current lack of consistent definitions for important concepts, such as hard and soft skills, and terms relating to skill extraction and classification.

Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings

Understanding preferences, opinions, and sentiment of the workforce is paramount for effective employee lifecycle management. 
Open-ended survey responses serve as a valuable source of information. 
This paper proposes a machine learning approach for aspect-based sentiment analysis (ABSA) of Dutch open-ended responses in employee satisfaction surveys. 
Our approach aims to overcome the inherent noise and variability in these responses, enabling a comprehensive analysis of sentiments that can support employee lifecycle management. 
Through response clustering we identify six key aspects (salary, schedule, contact, communication, personal attention, agreements), which we validate by domain experts. 
We compile a dataset of 1,458 Dutch survey responses, revealing label imbalance in aspects and sentiments. 
We propose few-shot approaches for ABSA based on Dutch BERT models, and compare them against bag-of-words and zero-shot baselines.
Our work significantly contributes to the field of ABSA by demonstrating the first successful application of Dutch pre-trained language models to aspect-based sentiment analysis in the domain of human resources (HR).

Aspect-Based Sentiment Analysis for Open-Ended HR Survey Responses

Skill Extraction involves identifying skills and qualifications mentioned in documents such as job postings and resumes. The task is commonly tackled by training supervised models using a sequence labeling approach with BIO tags. However, the reliance on manually annotated data limits the generalizability of such approaches. 
Moreover, the common BIO setting limits the ability of the models to capture complex skill patterns and handle ambiguous mentions. In this paper, we explore the use of in-context learning to overcome these challenges, on a benchmark of 6 uniformized skill extraction datasets. Our approach leverages the few-shot learning capabilities of large language models (LLMs) to identify and extract skills from sentences. We show that LLMs, despite not being on par with traditional supervised models in terms of performance, can better handle syntactically complex skill mentions in skill extraction tasks.

Rethinking Skill Extraction in the Job Market Domain using Large Language Models

Recent approaches in skill matching, employing synthetic training data for classification or similarity model training, have shown promising results, reducing the need for time-consuming and expensive annotations. However, previous synthetic datasets have limitations, such as featuring only one skill per sentence and generally comprising short sentences. In this paper, we introduce JobSkape, a framework to generate synthetic data that tackles these limitations, specifically designed to enhance skill-to-taxonomy matching. Within this framework, we create SkillSkape, a comprehensive open-source synthetic dataset of job postings tailored for skill-matching tasks. We introduce several offline metrics that show that our dataset resembles real-world data. Additionally, we present a multi-step pipeline for skill extraction and matching tasks using large language models (LLMs), benchmarking against known supervised methodologies. We outline that the downstream evaluation results on real-world data can beat baselines, underscoring its efficacy and adaptability.

JobSkape: A Framework for Generating Synthetic Job Postings to Enhance Skill Matching

Recent advancements in Large Language Models (LLMs) have been reshaping Natural Language Processing (NLP) task in several domains. Their use in the field of Human Resources (HR) has still room for expansions and could be beneficial for several time consuming tasks. Examples such as time-off submissions, medical claims filing, and access requests are noteworthy, but they are by no means the sole instances. However the aforementioned developments must grapple with the pivotal challenge of constructing a high-quality training dataset. On one hand, most conversation datasets are solving problems for customers not employees. On the other hand, gathering conversations with HR could raise privacy concerns. To solve it, we introduce HR-Multiwoz, a fully-labeled dataset of 550 conversations spanning 10 HR domains. Our work has the following contributions:
(1) It is the first labeled open-sourced conversation dataset in the HR domain for NLP research. (2) It provides a detailed recipe for the data generation procedure along with data analysis and human evaluations. The data generation pipeline is transferrable and can be easily adapted for labeled conversation data generation in other domains. (3) The proposed data-collection pipeline is mostly based on LLMs with minimal human involvement for annotation, which is time and cost-efficient.

HR-MultiWOZ: A Task Oriented Dialogue (TOD) Dataset for HR LLM Agent

Large language models have emerged as a useful technology for job matching, for both candidates and employers. Job matching is often based on a particular geographic location, such as a city or region. However, LMs have known biases, commonly derived from their training data. In this work, we aim to quantify the metropolitan size bias encoded within large language models, evaluating zero-shot salary, employer presence, and commute duration predictions in 384 of the United States’ metropolitan regions. Across all benchmarks, we observe correlations between metropolitan population and the accuracy of predictions, with the smallest 10 metropolitan regions showing upwards of 300% worse benchmark performance than the largest 10.

Big City Bias: Evaluating the Impact of Metropolitan Size on Computational Job Market Abilities of Language Models

This tutorial provides an overview of current approaches, problems and challenges in the detection of lexical semantic change. At its core, computational modelling of semantic change consists of the following:
- Modelling word meaning, typically using unsupervised methods applied to diachronic corpora;
- modelling meaning change, based on the results of the above; and
- evaluation.

The tutorial will provide an introduction to lexical semantic change and an overview of the resources available (corpora, pre-trained diachronic models and data sets). We will highlight issues related to the creation and use of diachronic corpora and different procedures for annotating data. We will then present the current state of the art in automatic detection of LSC, provide a hands-on section on available systems and tools, and open the floor for discussion on possible applications.

Computational Modeling of Semantic Change

This tutorial will introduce the NLP community to Item Response Theory (IRT). IRT is a method from the field of psychometrics for model and dataset assessment. IRT has been used for decades to build test sets for human subjects and estimate latent characteristics of dataset examples. Recently, there has been an uptick in work applying IRT to tasks in NLP. It is our goal to introduce the wider NLP community to IRT and show its benefits for a number of NLP tasks. From this tutorial, we hope to encourage wider adoption of IRT among NLP researchers.

Item Response Theory for Natural Language Processing

Join us for this introductory tutorial targetted at NLP researchers with no knowledge of chemistry as we explore cutting-edge new work in the integration of molecules and natural language, with exciting applications such as developing new drugs, materials, and chemical processes.

Climate change, access to food and water, pandemics— these words, when uttered, immediately summon to mind global challenges with possible disastrous outcomes. Molecular solutions will be critical to addressing these global problems on scales of complexity never-before-seen. However, they exist in extremely large search spaces, which makes AI tools a necessity. Excitingly, the chemistry field is posed to be substantially accelerated via multimodal models combining language with molecules and drug structures.

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